改进布谷鸟算法优化极限学习机的网络入侵检测

为了保证网络的安全,针对极限学习机在网络入侵检测过程中参数优化的难题,提出一种改进布谷鸟搜索算法优化极限学习机的网络入侵检测模型。首先将极限学习机参数编码为布谷鸟巢位置,并以网络入侵检测正确率作为ELM参数优化目标,然后通过模拟布谷鸟繁育行为找到极限学习机的最优参数,建立网络入侵检测分类器,最后在Matlab 2012平台上采用KDD99数据集进行仿真实验。结果表明,MCS-ELM提高了网络入侵检测正确率,可以满足网络入侵检测在线检测要求。     

SVELTE: Real-time intrusion detection in the Internet of Things

In the Internet of Things (IoT), resource-constrained things are connected to the unreliable and untrusted Internet via IPv6 and 6LoWPAN networks. Even when they are secured with encryption and authentication, these things are exposed both to wireless attacks from inside the 6LoWPAN network and from the Internet. Since these attacks may succeed, Intrusion Detection Systems (IDS) are necessary. Currently, there are no IDSs that meet the requirements of the IPv6-connected IoT since the available approaches are either customized for Wireless Sensor Networks (WSN) or for the conventional Internet.In this paper we design, implement, and evaluate a novel intrusion detection system for the IoT that we call SVELTE. In our implementation and evaluation we primarily target routing attacks such as spoofed or altered information, sinkhole, and selective-forwarding. However, our approach can be extended to detect other attacks. We implement SVELTE in the Contiki OS and thoroughly evaluate it. Our evaluation shows that in the simulated scenarios, SVELTE detects all malicious nodes that launch our implemented sinkhole and/or selective forwarding attacks. However, the true positive rate is not 100%, i.e., we have some false alarms during the detection of malicious nodes. Also, SVELTE’s overhead is small enough to deploy it on constrained nodes with limited energy and memory capacity.     

云计算、物联网、智慧地球及其相关法律问题

本文分别简单介绍云计算、物联网和智慧球三者的概念及发展状况,并对三者可能涉及的法律问题展开讨论。云计算虽处于发展初期,但已有一定规模的应用;物联网技术相对比较成熟,电子标签已得到广泛应用;智慧地球是一种建立在云计算和物联网基础之上的未来地球发展的战略模式。云计算、物联网和智慧地球因其自身的技术牦羔,存在隐私、安全等方面的隐患。解决这些问题除了依靠技术手段,相关的法律制定尤为重要。     

物联网：Web3．0时代的变革力量

物联网是信息技术积累到今天的一个发展机遇,是继个人计算机、互联网之后世界信息产业的第三次变革,随着信息技术的发展,人们即将进入物联网时代。物联网是指把所有物品通过射频识别等信息传感设备与互联网连接起来实现智能化识别和管理的网络。在物联网的基础上,“感知中国”、”智慧地球”将成为现实。     

Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things (IoT), machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT. By feeding previous power electronic data into the learning model, accurate information is drawn, and the quality of IoT-based power services is improved. Generally, the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology. The efficient execution of this data-intensive Power Workflow (PW) needs massive computing resources, which are available in the cloud infrastructure. Nevertheless, the execution efficiency of PW decreases due to inappropriate sub-task and data placement. In addition, the power consumption explodes due to massive data acquisition. To address these challenges, a PW placement method named PWP is devised. Specifically, the Non-dominated Sorting Differential Evolution (NSDE) is used to generate placement strategies. The simulation experiments show that PWP achieves the best trade-off among data acquisition time, power consumption, load distribution and privacy preservation, confirming that PWP is effective for the placement problem.     

基于物联网与云计算服务的农业温室智能化平台研究与应用

以建设智能化温室物联网为目标,提出温室物联网服务平台技术方案.该方案包括技术架构、温室环境智能监控系统、无线传感网及通信终端、智能控制终端和无线传感网及通信终端,智能控制终端.设计了温室环境监控软件,视频监控系统,农业小气候监测系统,监控中心,实现了温室群的数据存储、管理控制和云数据分析等功能.提高了温室生产的精细化作业水平.     

浅谈物联网技术与应用

物联网是互联网的应用拓展,被称为继计算机、互联网之后世界信息产业发展的第三次浪潮.介绍了物联网的起源、发展现状及基本定义,分析了物联网研究中的关键技术,并提出目前存在的一些问题.     

Machine learning for internet of things data analysis: a survey

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.     

基于IAP云平台的电力物联网

智能电网和物联网是当今各国的研究热点.基于IAP云平台的电力物联网致力于撬动生产力的变革,直接将自动化系统的运行过程呈现在网络化协同服务平台,不仅提升设备互联运行的协同效率,也大幅提升工人及运维人员工作效率,实现减少用工成本的同时提高生产效率.     

基于云计算平台的网络入侵检测算法研究与实现

利用并行化思想,在现有入侵检测算法GA遗传算法和BP神经算法的基础上,提出基于云计算平台的网络入侵检测算法MR GA-BP均值法算法,并对算法原理进行描述、建立、设计,系统能够针对海量入侵的检测数据进行实时学习和检测,对比传统入侵检测系统,在效率和精度上有了较大提高,通过两组对比实验,证明了该优化算法MR GA-BP均值法算法在入侵检测系统中的可行性以及性能优势。     

改进蝙蝠算法优化极限学习机的图像分类

针对分类器中的极限学习机参数优化问题,本文提出一种改进蝙蝠算法优化极限学习机的图像分类模型。首先将极限学习机参数看作蝙蝠位置,然后采用改进蝙蝠算法进行求解。采用病毒群体感染主群体,主群体在历代个体间纵向传递信息,病毒群体通过感染操作在同代个体间横向传递信息,增强了算法跳出局部极小值的能力。最后根据最优参数建立图像分类模型,并对模型的性能进行仿真测试。仿真结果表明,相对于对比模型,本文模型不仅提高了图像分类正确率,而且加快了分类速度,是一种有效的图像分类模型。     

Prerequisites between learning objects: Automatic extraction based on a machine learning approach

One standing problem in the area of web-based e-learning is how to support instructional designers to effectively and efficiently retrieve learning materials, appropriate for their educational purposes. Learning materials can be retrieved from structured repositories, such as repositories of Learning Objects and Massive Open Online Courses; they could also come from unstructured sources, such as web hypertext pages. Platforms for distance education often implement algorithms for recommending specific educational resources and personalized learning paths to students. But choosing and sequencing the adequate learning materials to build adaptive courses may reveal to be quite a challenging task.In particular, establishing the prerequisite relationships among learning objects, in terms of prior requirements needed to understand and complete before making use of the subsequent contents, is a crucial step for faculty, instructional designers or automated systems whose goal is to adapt existing learning objects to delivery in new distance courses. Nevertheless, this information is often missing. In this paper, an innovative machine learning-based approach for the identification of prerequisites between text-based resources is proposed. A feature selection methodology allows us to consider the attributes that are most relevant to the predictive modeling problem. These features are extracted from both the input material and weak-taxonomies available on the web. Input data undergoes a Natural language process that makes finding patterns of interest more easy for the applied automated analysis. Finally, the prerequisite identification is cast to a binary statistical classification task. The accuracy of the approach is validated by means of experimental evaluations on real online coursers covering different subjects.     

基于雾计算的物联网架构研究

物联网是一种能将物体连接至互联网使其更加智能的技术.但是物联网设备产生的大数据难以处理,网络架构的可扩展性差,以及用户的安全隐私容易泄露等问题都限制了物联网的发展.为了解决这些问题,通过分析雾计算所具有的优势提出基于雾计算的物联网架构.基于该架构,同时考虑到用户的安全隐私问题,又提出分层的网络架构.最后对文章进行总结和展望.     

基于CLDNN的物联网设备个体识别

针对目前个体识别中特征工程存在信号序列长、特征鲁棒性差等问题,研究了基于深度神经网络的个体识别技术。借鉴语音识别中的卷积长短时全连接神经网络(CLDNN),通过卷积神经网络提取信号的局部幅度特征,通过长短期记忆网络提取信号的全局时域特征,使用全连接网络实现特征图到设备标签的映射。在视距(LOS)信道下,采集8台LoRa调制的无线数传电台数据,加入高斯白噪声进行仿真测试。仿真表明,本文所提方法在信号序列长度为2 048点和低信噪比(0 dB)时,模型准确率达到95%;此外相较VGG16模型,本模型参数更少,在物联网设备部署方面具有一定的应用前景。     

基于物联网的节水灌溉自控系统研究

为提高农田灌溉用水利用率、降低灌溉用水成本,提出一种基于无线传感器网络和Internet技术的农田自动灌溉控制方法。重点分析了传感器网络节点路由协议,实现了系统硬件与软件设计,将各种传感器构成智能化传感器网络,从而全面提升了系统的自动化与监测水平。最后分析了该网络在Internet基础上实现某农厂的农田节水灌溉自控系统的实现方法,用户使用手机或无线PDA就可以方便地进行土壤含水量的在线监测与控制,实现了灌溉自动化。应用结果表明,系统通过嵌入式控制技术完成智能化灌溉,有助于改善农业灌溉用水的利用率和灌溉系统的自动化水平普遍较低的现状,可很好地实现节水。     

基于机器学习的物联网异常流量检测方法

随着物联网的广泛应用,物联网的安全问题受到越来越多的关注.针对物联网环境下异常网络流量问题,提出了基于机器学习的物联网异常流量检测方法.首先通过使用聚类算法分析物联网一段时间内网络数据的特征,然后使用连续假设检验算法对特征进行分类,并对恶意流量的空间分布进行二次特征分析.实验表明,相对于传统的异常流量检测方法,该检测方...     

基于云计算的物联网数据挖掘模式的构建

科技的不断发展和互联网技术的不断进步使得物联网给人类的联通需求提供了更多的帮助,同时也对该项技术对数据挖掘的模式提出了更高要求。而传统的数据挖掘显然已经无法满足物联网的发展要求。基于此,利用云计算技术衍生出来的物联网数据挖掘受到了更广泛的关注。     

基于物联网的矿井温、湿度检测系统设计

为了实现对矿井温、湿度检测,提出了一种基于物联网的矿井温、湿度检测系统,并完成了系统的软硬件测试。硬件包括由分布在矿井中的测量节点组成的无线传感网络,由基站节点和监控终端设备相连接形成的监控装置,软件采用MSP430进行编程,并实现了温湿度信号检测。实际应用表明,该系统具有体积小,部署方便,成本低廉等优点。     

Data comparable encryption scheme based on cloud computing in Internet of things

The previously proposed short comparable encryption (SCE) scheme can infer the plaintext relationship by comparing the ciphertexts relationship as well as ensuring data security in Internet of things.Unfortunately,it will incur high storage and computational burden during the process of comparing ciphertexts and generating tokens.To this end,an efficient short comparable encryption scheme called SCESW was proposed,which was utilizing the sliding window method with the same size window.Formal security analysis shows that the scheme can guarantee weak indistinguishability in standard model as well as data security and integrity.The experimental results demonstrate that the storage of the SCESW scheme is $\frac{1}{t}(t＞1)$ times shorter than that of the SCE scheme and the efficiency of the SCESW scheme is superior to that of the SCE scheme.     

深度强化学习在物联网边缘计算中的应用研究

针对传统的物联网边缘计算方法存在计算成本过高,计算时间过长等问题,文中引入了深度强化学习技术,对物联网边缘计算方法进行优化.通过物联网拓扑结构设定物联网边缘计算周期,获取数据上传速度.设计边缘计算执行过程,提升边缘计算资源分配效率.引入深度强化学习技术中的CNN模型实现卷积计算,完成物联网边缘计算的资源分配.至此,实现...